WEBVTT 00:00.530 --> 00:06.770 I realized my enthusiasm for this project is a bit insane, but I have to tell you that I am very satisfied 00:06.770 --> 00:07.670 with the outcome. 00:07.700 --> 00:12.620 It's rare that you come up with a project like this that has so many moving parts and is technically 00:12.620 --> 00:20.240 quite complex and has a lot of substance to it, but have such a simple and tangible outcome as our 00:20.240 --> 00:22.340 push notification for a good deal. 00:22.520 --> 00:29.750 There is something beyond the king of the notification that really makes this very satisfying indeed. 00:30.290 --> 00:35.660 But one point that I wanted to make about it, perhaps a more, more seriously, is to point out that 00:35.660 --> 00:41.120 this sort of template that we've got here for solving this problem is something that you can apply to 00:41.150 --> 00:46.940 a lot of other similar kinds of business problems and startup ideas and commercial problems that you 00:46.940 --> 00:48.680 might even have been thinking about already. 00:48.710 --> 00:53.570 I mean, to give you something that comes to mind right away is that when I was looking on hugging face 00:53.570 --> 01:00.020 for the data for this this project, for the Amazon pricing data, I also came across a bunch of data 01:00.020 --> 01:07.670 that that has company reports, annual reports from companies and shows how that relates to the change 01:07.670 --> 01:14.610 in share price on the day and the few days after the report comes out, which is a very interesting 01:14.610 --> 01:15.420 data set. 01:15.420 --> 01:22.110 And it occurs to me that whilst this is more of a stretch, it is also obviously a bigger opportunity 01:22.110 --> 01:23.580 if you can find a way to crack it. 01:23.610 --> 01:30.810 If there's a way to build a proprietary model that's able to look at that and make some prediction about 01:30.810 --> 01:35.130 what will happen to the share price as a result of a company's report being published. 01:35.160 --> 01:41.910 And you could imagine then having a scanner agent that scans the new company reports being being being 01:41.910 --> 01:42.630 released. 01:42.660 --> 01:49.170 And so you can really see the very similar agent workflow that you would adapt for that kind of of of 01:49.170 --> 01:49.710 problem. 01:49.710 --> 01:54.660 Now, of course, I will tell you that any trading decisions that you make are entirely at your own 01:54.660 --> 01:56.880 risk, and I have nothing to do with them at all. 01:57.060 --> 02:03.120 And obviously no one has yet cracked that problem of being able to build an automated AI system that 02:03.120 --> 02:09.000 can do things like read company reports and make, uh, trading decisions robust trading decisions as 02:09.000 --> 02:09.510 a result. 02:09.510 --> 02:15.810 But it seems like a serious, a very, um, important challenge to take on. 02:15.810 --> 02:21.060 And I wanted to point that out because it shows you how similar that is in terms of the the bits and 02:21.060 --> 02:24.360 pieces of the puzzle that you would look at if you wanted to do that. 02:24.360 --> 02:33.160 So that could certainly be a big challenge, if nothing else, just for the interest of seeing what 02:33.160 --> 02:38.200 happens and seeing whether you can find signal in that kind of data and using it as an opportunity to 02:38.230 --> 02:40.600 build a similar agent workflow. 02:41.410 --> 02:42.040 Okay. 02:42.070 --> 02:49.930 Anyway, with that, let's move to the last slide of this week, which is of this sorry, not of this 02:49.930 --> 02:54.430 week, of this day, which is to tell you that there is now one day left. 02:54.430 --> 02:59.890 There is one final day, week eight, day five, which will be the conclusion of this course. 02:59.890 --> 03:01.930 And you don't want to miss it. 03:02.050 --> 03:07.210 Uh, before I tell you what you'll do, then just to remind you, at this point you can generate code 03:07.210 --> 03:13.510 and text with frontier models, with open source models, using hugging face, using APIs with tools, 03:13.510 --> 03:18.760 structured outputs, rag with assistance from from Gradio. 03:18.790 --> 03:25.180 You can follow that strategy we went through to solve problems including curating your data, which 03:25.180 --> 03:30.280 is hard and grueling, but perhaps the single most important step. 03:30.460 --> 03:34.330 Um, making your baseline model and fine tuning a frontier model. 03:34.330 --> 03:38.380 And then you can Select and train an open source model. 03:38.380 --> 03:39.370 And we did it. 03:39.400 --> 03:42.010 We beat the frontier when we did that. 03:42.040 --> 03:44.770 You can deploy models to production. 03:44.770 --> 03:48.730 Now you can use modal to to put something up there on the cloud. 03:48.760 --> 03:53.980 You can also productionize your code in other ways and prepare it into something that can be run in 03:53.980 --> 03:55.750 production environments. 03:56.140 --> 04:03.400 And now you can build agentic workflows with planning memory, databases and multiple agents working 04:03.430 --> 04:07.030 together to take a complex task and break it down. 04:07.360 --> 04:11.470 And so that brings us to preparing for tomorrow. 04:11.470 --> 04:16.780 Tomorrow, of course, we're going to be building a user interface, which is going to be glorious because 04:16.780 --> 04:17.920 it's in Gradio. 04:18.040 --> 04:23.980 Uh, we'll also be looking at the fact that today when we ran that workflow, you'll notice that it 04:23.980 --> 04:25.270 ran and then it completed. 04:25.270 --> 04:30.730 So it's not really autonomous and it doesn't keep running and have it still relies on the human to press 04:30.730 --> 04:31.330 it each time. 04:31.330 --> 04:32.950 So we need to fix that of course. 04:32.950 --> 04:35.320 So we will do both of those things tomorrow. 04:35.320 --> 04:43.270 And once they are done, that will leave you in a position where you will have mastered AI and LM engineering. 04:43.330 --> 04:45.100 And I can't wait for that point. 04:45.100 --> 04:49.300 I hope that you are both excited and proud and I will see you there.